Face feature extraction: a complete review
H Wang, J Hu, W Deng - IEEE Access, 2017 - ieeexplore.ieee.org
Feature extraction is vital for face recognition. In this paper, we focus on the general feature
extraction framework for robust face recognition. We collect about 300 papers regarding face …
extraction framework for robust face recognition. We collect about 300 papers regarding face …
Label consistent K-SVD: Learning a discriminative dictionary for recognition
A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse
coding is presented. In addition to using class labels of training data, we also associate label …
coding is presented. In addition to using class labels of training data, we also associate label …
Sparse representation based fisher discrimination dictionary learning for image classification
The employed dictionary plays an important role in sparse representation or sparse coding
based image reconstruction and classification, while learning dictionaries from the training …
based image reconstruction and classification, while learning dictionaries from the training …
Fisher discrimination dictionary learning for sparse representation
Sparse representation based classification has led to interesting image recognition results,
while the dictionary used for sparse coding plays a key role in it. This paper presents a novel …
while the dictionary used for sparse coding plays a key role in it. This paper presents a novel …
Discriminative K-SVD for dictionary learning in face recognition
In a sparse-representation-based face recognition scheme, the desired dictionary should
have good representational power (ie, being able to span the subspace of all faces) while …
have good representational power (ie, being able to span the subspace of all faces) while …
Learning a discriminative dictionary for sparse coding via label consistent K-SVD
A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse
coding is presented. In addition to using class labels of training data, we also associate label …
coding is presented. In addition to using class labels of training data, we also associate label …
Sparse modeling for image and vision processing
In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …
models and their applications. In statistics and machine learning, the sparsity principle is …
Supervised dictionary learning and sparse representation-a review
MJ Gangeh, AK Farahat, A Ghodsi… - arXiv preprint arXiv …, 2015 - arxiv.org
Dictionary learning and sparse representation (DLSR) is a recent and successful
mathematical model for data representation that achieves state-of-the-art performance in …
mathematical model for data representation that achieves state-of-the-art performance in …
Dictionary learning based software defect prediction
In order to improve the quality of a software system, software defect prediction aims to
automatically identify defective software modules for efficient software test. To predict …
automatically identify defective software modules for efficient software test. To predict …
Learning structured low-rank representations for image classification
An approach to learn a structured low-rank representation for image classification is
presented. We use a supervised learning method to construct a discriminative and …
presented. We use a supervised learning method to construct a discriminative and …